A Novel Data Independent Approach for Conversion of Hand Punched Kannada Braille Script to Text and Speech

In society, there exists a gap in communication between the sighted community and the visually challenged people due to different scripts followed to read and write. To bridge this gap there is a need for a system that supports automatic conversion of Braille script to text and speech in the corresponding language. Optical Braille Recognition (OBR) system converts the hand-punched Braille characters into their equivalent natural language characters. The Text-to-Speech (TTS) system converts the recognized characters into audible speech using speech synthesis techniques. Existing literature reveals that OBR and TTS systems have been well established independently for English. There is a scope for development of OBR and TTS systems for regional languages. In spite of Kannada being one of the most widely spoken regional languages in India, minimal work has been done towards Kannada OBR and TTS. There is no system that directly converts Braille script to speech, therefore, this development of Kannada Braille to text and speech system is one of a kind. The acquired image is processed and feature extraction is performed using [Formula: see text]-means algorithm and heuristics to convert the Braille characters to Kannada script. The concatenation based speech synthesis technique employing phoneme as the basic unit is used to convert Kannada TTS using Festival TTS framework. Performance evaluation of the proposed system is done using Kannada Braille database developed independently, and the results obtained are found to be satisfactory when compared to existing methods in the literature.

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The Feature Extraction Algorithm for the Production of Emotions in Text-to-Speech (TTS) System for an Indian Regional Language

Pervasive Computing for Business ◽

10.4018/978-1-60566-996-0.ch003 ◽

2010 ◽

pp. 17-30

Author(s):

Jagadish S Kallimani ◽

V. K Ananthashayana ◽

Debjani Goswami

Keyword(s):

Feature Extraction ◽

Language Processing ◽

Speech Synthesis ◽

Text To Speech ◽

Everyday Objects ◽

Regional Language ◽

Extraction Algorithm ◽

Processing Signal ◽

Emotional Aspects ◽

Text To Speech Synthesis

Text-to-speech synthesis is a complex combination of language processing, signal processing and computer science. Ubiquitous computing (ubicomp) is a post-desktop model of human-computer interaction in which information processing has been thoroughly integrated into everyday objects and activities. Speech synthesis is the generation of synthesized speech from text. This chapter deals with the development of a Text to Speech (TTS) Synthesis system for an Indian regional language by considering Bengali as the language. This chapter highlights various methods which may be used for speech synthesis and also it provides an overview on the problems and difficulties in Bengali text to speech conversion. Variations in the prosody (speech parameters – volume, pitch, intonation, amplitude) of the speech yields the emotional aspects (anger, happy, normal), which are applied to our developed TTS system.

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Speech synthesis for text-to-speech alignment and prosodic feature extraction

Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97 ◽

10.1109/iscas.1997.612866 ◽

2002 ◽

Cited By ~ 1

Author(s):

F. Malfrere ◽

T. Dutoit

Keyword(s):

Feature Extraction ◽

Speech Synthesis ◽

Text To Speech ◽

Prosodic Feature ◽

Speech Alignment

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Text to Speech Synthesis System for Punjabi language using Statistical Parametric Speech Synthesis Technique

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1042.0789s19 ◽

2019 ◽

Vol 8 (9S) ◽

pp. 268-272

Keyword(s):

Speech Synthesis ◽

Gaussian Mixture ◽

Indian Languages ◽

Text To Speech ◽

Synthesis Technique ◽

Text To Speech Synthesis ◽

Statistical Parametric Speech Synthesis ◽

Parametric Speech Synthesis ◽

Traditional Approaches

Statistical Parametric Speech Synthesis has been most growing technique rather than the traditional approaches that we are used to synthesizing the speech. The shortcoming of traditional approaches will be overcome with latest statistical techniques. The main advantages of SPSS from traditional synthesis technique are that it has more flexibility to change the characteristics of voice and support more multiple languages i.e. multilingual, has good coverage of acoustic ` and robustness. It generates high quality of speech from small training database. Deep Neural network and Hidden Morkov model are basic statistical parametric speech synthesis techniques. Gaussian mixture model, sinusoidal model are also under this categories. Features were extracted in two type spectral features like spectral bandwidth, spectral centroid etc. and excitation features like F0 frequencies etc. We are using 722 Punjabi phonemes. Using sound forge software we extracted the 200 wave file from 1 hour pre-recording wave file related to those phonemes. Each and every phonemes feature was extracted and saved in database. We were extracting 28 features of each phoneme. TTS text-to-speech system generates sounds or speech as a output when provided the text of Punjabi language. There were already many TTS are developed on different Indian languages. The system that we are trying to build is based only on Punjabi language.

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Smart Reader for Visually Challenged Using Optical Character Recognition and Text-To-Speech

10.46532/978-81-950008-1-4_045 ◽

2020 ◽

pp. 205-208

Author(s):

Sowmya R ◽

Sushma S Jagtap ◽

Gnanamoorthy Kasthuri

Keyword(s):

Character Recognition ◽

Optical Character Recognition ◽

Speech Synthesis ◽

Read Aloud ◽

Text To Speech ◽

Digital Text ◽

Optical Character ◽

The People ◽

Text To Speech Synthesis ◽

Visually Challenged

Assistive technology uses assistive, adaptive and rehabilitative devices for people with disabilities. It’s assessed there are about 36 million people with visual impairment in the world and a further 216 million who lead life with moderate to severe visual impairments. Leveraging technology has helped the visually challenged in carrying out tasks on par with the people blessed with vision particularly in the activities of reading and writing. In the proposed work, an image scanning device attached to a microcontroller is designed. This device is designed in the form of hand gloves for ease of usage. The glove with the camera at the fingertip, when rolled over lines of text, scans the information and converts it into digital text with Optical Character Recognition (OCR). The converted digital text is finally read aloud using Text-to-speech synthesis. The results obtained were accurate and met the standards of operability.

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Note from the editor: “Expositions of Romanian scientists on the design of text-to-speech synthesis and natural language understanding and generation systems”

International Journal of Speech Technology ◽

10.1007/s10772-009-9063-3 ◽

2009 ◽

Vol 12 (2-3) ◽

pp. 59-61

Author(s):

Amy Neustein

Keyword(s):

Natural Language ◽

Speech Synthesis ◽

Natural Language Understanding ◽

Text To Speech ◽

Language Understanding ◽

Text To Speech Synthesis

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An Unit Selection based Hindi Text To Speech Synthesis System Using Syllable as a Basic Unit

IOSR Journal of VLSI and Signal processing ◽

10.9790/4200-04424957 ◽

2014 ◽

Vol 4 (4) ◽

pp. 49-57 ◽

Cited By ~ 2

Author(s):

Shreekanth. T ◽

Keyword(s):

Speech Synthesis ◽

Basic Unit ◽

Text To Speech ◽

Synthesis System ◽

Unit Selection ◽

Text To Speech Synthesis

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Design of English text-to-speech conversion algorithm based on machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189238 ◽

2020 ◽

pp. 1-12

Author(s):

Li Dongmei

Keyword(s):

Machine Learning ◽

Speech Synthesis ◽

Feature Recognition ◽

Learning Algorithm ◽

Morphological Structure ◽

English Text ◽

Text To Speech ◽

Part Of Speech ◽

Modern Computer ◽

Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

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